System for predicting properties of structures, imager system, and related methods

ABSTRACT

A method of predicting virtual metrology data for a wafer lot that includes receiving first image data from an imager system, the first image data relating to at least one first wafer lot, receiving measured metrology data from metrology equipment relating to the at least one first wafer lot, applying one or more machine learning techniques to the first image data and the measured metrology data to generate at least one predictive model for predicting at least one of virtual metrology data or virtual cell metrics data of wafer lots, and utilizing the at least one generated predictive model to generate at least one of first virtual metrology data or first virtual cell metrics data for the first wafer lot.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of U.S. patent application Ser. No.16/100,729, filed Aug. 10, 2018, now U.S. Pat. No. 10,872,403, issuedDec. 22, 2020, the disclosure of which is hereby incorporated herein inits entirety by this reference.

TECHNICAL FIELD

This disclosure relates generally to methods of generating andpredicting virtual metrology data and virtual cell metric data of wafersvia machine learning techniques based on image data. This disclosurealso relates to an image system for balancing intensities of colors froma light source.

BACKGROUND

Semiconductor devices and other microelectronic devices are typicallymanufactured on a workpiece configured as a wafer or other bulksubstrate comprising semiconductor material and having a large number ofindividual die locations in an array on an active surface. Each waferundergoes several different procedures to construct the switches,capacitors, conductive interconnects, and other components of a device.For example, a wafer is processed using lithography, implanting,etching, deposition, planarization, annealing, and other procedures thatare repeated to construct a high density of features in the form ofmicrostructures. One aspect of manufacturing microelectronic devices isevaluating the workpieces to ensure that the microstructures of each dielocation fall within the desired specifications. For example, processengineers must be able to accurately measure various critical dimensions(“CD”) and thicknesses of such surface features as well as surfacerecesses over the entire active surface of the wafer comprising the dielocations to fine tune the fabrication process and to assure a desireddevice geometry.

Scatterometry is one technique for evaluating several parameters (e.g.,critical dimensions and thicknesses) of microstructures. By way ofexample, scatterometry, a type of reflectometry, is a non-destructiveoptical technique that records and analyzes changes in intensity oflight reflected from a periodic scattering surface. By measuring andanalyzing the light diffracted from a patterned periodic sample, thedimensions of the periodic structure can be measured. In certain typesof scatterometry, light with a wide spectral composition can be directedonto a workpiece at a fixed angle, and the intensity of the lightchanges relative to changes in wavelength. With respect to semiconductordevices, scatterometry is used to evaluate film thickness, line spacing,trench depth, trench width, and other aspects of microstructures. Manysemiconductor wafers, for example, include gratings in the scribe lanesbetween the individual dies to provide a periodic structure that can beevaluated using existing scatterometry equipment. One existingscatterometry process includes illuminating such periodic structures ona workpiece and obtaining a representation of the scattered radiationreturning from the periodic structure. The representation of returnradiation is then analyzed to estimate one or more parameters of themicrostructure. Several different scatterometers and methods have beendeveloped for evaluating different aspects of microstructures and/orfilms on different types of substrates.

Some scatterometry systems include an optical relay system to receivethe reflected light and a sensor array to image the reflected light.International Publication No. WO 2005/026707 and U.S. Pat. Nos.6,804,001; 6,556,284; 5,880,845; and 5,703,686 disclose variousgenerations of scatterometers.

Ellipsometry is another technique for evaluating parameters (e.g.,critical dimensions and thicknesses) of microstructures. As is known inthe art, ellipsometry is an optical technique for investigating thedielectric properties (complex refractive index or dielectric function)of thin films. Ellipsometry measures a change of polarization uponreflection or transmission and compares it to a model. In particular,ellipsometry may be used to characterize composition, roughness,thickness (depth), crystalline nature, doping concentration, electricalconductivity and other material properties. Ellipsometry is verysensitive to changes in the optical response of incident radiation thatinteracts with the material being investigated.

When utilizing ellipsometry, the measured signal is the change inpolarization as the incident radiation (in a known state) interacts withthe material structure of interest (reflected, absorbed, scattered, ortransmitted). The polarization change is quantified by the amplituderatio, and the phase difference. Furthermore, because the signal dependson the thickness as well as the material properties, ellipsometry is aversatile tool for contact free determination of thickness and opticalconstants of films.

One challenge of assessing microstructures using scatterometry and/orellipsometry is that the measurements can only be performed on speciallydesigned scatter boxes in a scribe. Additionally, these processes aretypically relatively slow, and data from the scatter boxes can deviatefrom live die due to processing damage in the scribe. Additionally, thenecessary computational time, for example, can require several minutessuch that the workpieces (e.g., wafers) are typically evaluated offlineinstead of being evaluated in-situ within a process tool. For example,as will be appreciated by one of ordinary skill in the art, collectingmetrology data via a scatterometer and/or an ellipsometer is relativelytime consuming and, as a result, the time required to collect metrologydata prohibits how much metrology data can be collected and still remaincost effective. Accordingly, typically only a relatively small number ofdata points are collected for a limited number of wafers within a givenwafer lot. In other words, not every wafer within a wafer lot isanalyzed via a scatterometer and/or an ellipsometer during conventionalprocessing.

Additionally, as is known in the art, various forms of imagers are alsoutilized in evaluating parameters of microstructures of wafers.Typically, light emitting diode (“LED”) light is utilized as a lightsource and is emitted at a wafer. Reflected light is conventionallycollected by a complementary metal oxide (“CMOS”) imager. The CMOSimagers capture a composite image as well as a RGB image (e.g.,truecolor image) of the wafer. Utilizing a CMOS imager providesrelatively fast data collection and provides images of the entire wafer.However, although an RGB ratio acquired via the RGB image is relativelysensitive to thickness and critical dimensions, the data acquired viaCMOS imagers has not been used to extract critical dimensions andthickness data with the precision of traditional metrology tools (e.g.,scatterometry and/or ellipsometry).

Cell or probe metrics are electrical measurements performed onsemiconductor die to quantify parameters such as, for example, 1) medianthreshold voltage of a die, 2) variation in threshold voltage within adie, 3) operating voltage window of the die, 4) endurance to read/writecycles, 5) lifetime of the die, 6) persistence of the memory, and 7)product grade of the die. Typically, these measurements are achievableonly after all the processing steps are completed, which, in someinstance, can take several months. Variation of the foregoing parametersbetween wafers or between dies of the same wafers can be caused byvariation in processes like thickness and CDs.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed understanding of the present disclosure, reference shouldbe made to the following detailed description, taken in conjunction withthe accompanying drawings, in which like elements have generally beendesignated with like numerals, and wherein:

FIG. 1 illustrates a schematic diagram of an environment in which apredictive metrology and cell metrics system can operate according toone or more embodiments of the present disclosure;

FIG. 2 a simplified sequence-flow that a prediction system may utilizeto train a machine learning model, generate one or more predictivemodels related to metrology parameters and/or cell metrics, and predictmetrology parameters and/or cell metrics for at least one wafer;

FIGS. 3A and 3B show example comparisons of metrology data acquired viaconventional methods and virtual metrology data generated by theprediction system of the present disclosure;

FIGS. 4A and 4B show additional comparisons of metrology data acquiredvia conventional methods and virtual metrology data generated by theprediction system of the present disclosure;

FIGS. 5A and 5B show additional comparisons of metrology data acquiredvia conventional methods and virtual metrology data generated by theprediction system of the present disclosure;

FIG. 6 shows a schematic representation of an imager system according toone or more embodiments of the present disclosure;

FIG. 7 shows a graph depicting balanced intensities of colors withinwhite light;

FIG. 8 shows a top view of the imager system of FIG. 6 ; and

FIG. 9 illustrates a block diagram of an example computing device inaccordance with one or more embodiments of the present disclosure.

DETAILED DESCRIPTION

The illustrations presented herein are not actual views of any imagesystem, prediction system, or any component thereof, but are merelyidealized representations, which are employed to describe embodiments ofthe present invention.

As used herein, the singular forms following “a,” “an,” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise.

As used herein, the term “may” with respect to a material, structure,feature, or method act indicates that such is contemplated for use inimplementation of an embodiment of the disclosure, and such term is usedin preference to the more restrictive term “is” so as to avoid anyimplication that other compatible materials, structures, features, andmethods usable in combination therewith should or must be excluded.

As used herein, any relational term, such as “first,” “second,” “above,”etc., is used for clarity and convenience in understanding thedisclosure and accompanying drawings, and does not connote or depend onany specific preference or order, except where the context clearlyindicates otherwise. For example, these terms may refer to orientationsof elements of a prediction system, wafer, and/or imager system inconventional orientations. Furthermore, these terms may refer toorientations of elements of a prediction system, wafer, and/or imagersystem as illustrated in the drawings.

As used herein, the term “substantially” in reference to a givenparameter, property, or condition means and includes to a degree thatone skilled in the art would understand that the given parameter,property, or condition is met with a small degree of variance, such aswithin acceptable manufacturing tolerances. By way of example, dependingon the particular parameter, property, or condition that issubstantially met, the parameter, property, or condition may be at least90.0% met, at least 95.0% met, at least 99.0% met, or even at least99.9% met.

As used herein, the term “about” used in reference to a given parameteris inclusive of the stated value and has the meaning dictated by thecontext (e.g., it includes the degree of error associated withmeasurement of the given parameter, as well as variations resulting frommanufacturing tolerances, etc.).

As used herein, the term “wafer” means and includes materials upon whichand in which structures including feature dimensions of micrometer andnanometer scale are partially or completely fabricated. Such termsinclude conventional semiconductor (e.g., silicon) wafers, as well asbulk substrates of semiconductor and other materials. Such structuresmay include, for example, integrated circuitry (active and passive),MEMS devices, and combinations thereof. For the sake of convenience,such structures will be referenced below as “wafers.”

Embodiments of the present disclosure include a predictive metrology andcell metrics system that generates one or more predictive models forpredicting metrology and cell metrics of wafers based on image data andvia machine learning techniques. In some embodiments, the predictivesystem may train one or more predictive models based on image data andmeasured metrology data from one or more wafer lots. The image data mayinclude image data of a current state (e.g., layer) of the wafers of thegiven wafer lot (e.g., images collected after the most recent processingstep). Additionally, the image data may include current collected red,green, and blue (“RGB”) levels (e.g., intensities) and/or ratios of thewafers of the given wafer lot captured by an image system. Furthermore,the image data may include images of each of the wafer of the givenwafer lot at previous steps of processing (e.g., level steps). Forinstance, the image data may include images of each of the wafer of thegiven wafer during earlier processes (e.g., previous applied masks,laminations, etchings, exposures, patterning, packaging, etc.). Themetrology data may include measured metrology parameters such as,thicknesses of features (e.g., layers and/or films) of the given waferand/or dimensions of features (e.g., features patterned on a wafer byuse of photo-lithography, dry etch, wet etch, or other semiconductorprocessing techniques) of a wafer. Additionally, the image data and/ormetrology data may include data related to the cell metrics data, suchas, for example, median threshold voltage of a given die, variation inthreshold voltage within the given die, operating voltage window of thegiven die, endurance to read and/or write cycles, lifetime of the givendie, persistence of memory within the die, binning of the given die tovarious product grades, etc. In operation, the prediction system maytrain the one or more predictive models with the image data against themetrology data and/or cell metrics data. In other words, the predictionsystem may determine relationships between image data (e.g., color data)and the metrology data (e.g., wafer features).

Upon training (e.g., generating) the one or more predictive models,embodiments of the present disclosure include a prediction system thatpredicts (e.g., estimates) metrology data for a given wafer lot based onimage data related to the wafer lot and little to no metrology datautilizing the one or more predictive models. By applying the one or morepredictive models to the image data of a given wafer lot, the predictionsystem may determine and generate virtual metrology data (e.g.,predicted metrology data) for the given wafer lot. For example, based onthe image data for the given wafer lot, the prediction system may, viathe one or more generated predictive models, predict correlating virtualmetrology data and/or virtual cell metrics data of the given wafer lotwithout necessarily taking any measurements with metrology equipment. Insome embodiments, the virtual metrology data may include data related topredicted thicknesses and/or critical dimensions of features of thewafer lot. For example, the virtual metrology data may include datarelated to any of the measurements achievable via traditional metrologyequipment. Furthermore, the virtual metrology data may include datarelated to the cell metrics, such as, for example, median thresholdvoltage of a given die, variation in threshold voltage within the givendie, operating voltage window of the given die, endurance to read and/orwrite cycles, lifetime of the given die, persistence of memory withinthe die, binning of the given die to various product grades.

One or more embodiments of the present disclosure include an imagersystem. The imager system may be configured as any suitable imagersystem known in the art. However, the imager system may further includeone or more photo diodes to detect light emitted by a light source ofthe image system and provide feedback to a controller of the imagersystem. Based on the detected light, the controller may adjust (e.g.,tune) power (e.g., current) to LED banks within the light source tobalance intensities of the individual LED banks (e.g., the colors of thelights source). For example, as is discussed in greater detail below, arequirement of generating the virtual metrology described herein, is tohave consistency in output RGB values irrespective to which imager toolwas utilized to collect the image data or the location within the waferat which the image data was collected. Accordingly, the imager systemmay include mechanisms for calibrating intensities of individual colorswithin the light emitted by the LED banks. Additionally, the imagersystem is capable of calibrating light intensities at differentlocations within an illumination area of the imager system.

In view of the foregoing, one of ordinary skill in the art willappreciate that the complexity of multiple layers of films andpatterning in a semiconductor die previously prevented creating amathematical model that could be used to calculate film thickness andCD. Therefore, although variability in thicknesses and CD ofsemiconductor die could previously be observed empirically from colordifferences, actual measurements were not previously achievable on aproduct full flow die. Furthermore, predictability of metrics based oninline imaging may assist in optimizing specifications and may assist indetecting drift before a given wafer is probed to take an actualmeasurement.

FIG. 1 illustrates a schematic diagram of an environment 100 in which apredictive metrology and cell metrics system 102 (hereinafter“prediction system 102”) may operate according to one or moreembodiments of the present disclosure. In one or more embodiments, theprediction system 102 operates within, or in conjunction with, metrologyequipment 104 and an imager system 106. Additionally, the environment100 may include a user device 110 having an application 112. In someembodiments, the prediction system 102, the metrology equipment 104, theimager system 106, and the user device 110 can communicate via anetwork.

In some embodiments, the metrology equipment 104 may include one or moreof a scatterometer and/or an ellipsometer. For instance, the metrologyequipment 104 may include any of the scatterometry and/or ellipsometrysystems described above. Additionally, the metrology equipment 104 mayinclude any known scatterometry, ellipsometry systems, and/or CDscanning electron microscopy (“SEM”) systems. Additionally, themetrology equipment 104 may be operably coupled to the prediction system102 and may provide measured metrology data (e.g., data related tomeasurements of thicknesses and critical dimensions of features ofwafer) to the prediction system 102. Furthermore, the metrologyequipment 104 may be operated in conjunction and coordination with theimager system 106.

The imager system 106 may include any known imagers utilized for imagingsemiconductor devices. For instance, the imager system 106 may include acomplementary metal oxide (“CMOS”) imager, as described above. Asanother non-limiting example, the imager system 106 may include a waferintelligent scanner. For example, the imager system 106 may form aportion of a CLEAN TRACK™ LITHIUS Pro™ platform. For instance, theimager system 106 may include a Wafer Intelligent Scanner (“WIS”). Inone or more embodiments, the imager system 106 may be integrated in aphoto track (e.g., a photolithographic tool) or may include standaloneindividual equipment. As another non-limiting example, the imager system106 may include an AMAT® (Applied Materials) imager. In someembodiments, the imager system 106 may be integrated withinchemical-mechanical planarization tools. Additionally, the imager system106 may be operably coupled to the prediction system 102 and may provideimage data to the prediction system 102.

As is described in greater detail below in regard to FIGS. 2-5B, theprediction system 102 may receive measured metrology data and image datafrom the metrology equipment 104 and imager system 106, respectively,for one or more wafer lots. Furthermore, based on the received measuredmetrology data and image data and utilizing the machine learning system,the prediction system 102 may generate one or more predictive models forpredicting metrology parameters and/or cell metrics for wafer lots. Asused herein the term “metrology parameters” may refer to thickness andcritical dimensions data for a given wafer. For instance, metrologyparameters may refer to thicknesses of features (e.g., layers and/orfilms) of the given wafer and/or dimensions of features of the givenwafer (e.g., features patterned on the given wafer by use ofphoto-lithography, dry etch, wet etch, or other semiconductor processingtechniques). As used herein, the term “cell metrics” may refer to metricrepresenting cell health. For instance, cell metrics (e.g., electricalperformance metrics) may refer to median threshold voltage of a givendie, variation in threshold voltage within the given die, operatingvoltage window of the given die, endurance to read and/or write cycles,lifetime of the given die, persistence of memory within the die, binningof the given die to various product grades, etc. Furthermore, utilizingthe generated predictive models and image data available for given waferlots, the prediction system 102 may predict one or more metrologyparameters and/or cell metrics for the given wafer lots when minimal tono metrology data is available and/or collected for the given waferlots.

As illustrated in FIG. 1 , a user 111 can interface with the user device110, for example, to utilize the prediction system 102 to generatevirtual metrology data and/or virtual cell metrics data. The user 111can be an individual (i.e., human user), a business, a group, or anyother entity. Although FIG. 1 illustrates only one user 111 associatedwith the user device 110, the environment 100 may include any number ofusers that each may interact with the environment 100 using acorresponding client device.

In some embodiments, the user device 110 includes a client application112 installed thereon. The client application 112 can be associated withthe prediction system 102. For example, the client application 112allows the user device 110 to directly or indirectly interface with theprediction system 102. The client application 112 also enables the user111 to initiate analysis of wafer lots via the imager system 106,metrology equipment 104, and prediction system 102 (e.g., one or morepredictive models) and the user device 110 to receive predictedmetrology and cell metrics data.

Both the user device 110 and the prediction system 102 represent varioustypes of computing devices with which users can interact. For example,the user device 110 and/or the prediction system 102 can be a mobiledevice (e.g., a cell phone, a smartphone, a PDA, a tablet, a laptop, awatch, a wearable device, etc.). In some embodiments, however, the userdevice 110 and/or the prediction system 102 can be a non-mobile device(e.g., a desktop or server). Additional details with respect to the userdevice 110 and the prediction system 102 are discussed below withrespect to FIG. 7 .

FIG. 2 shows an example process 200 of the prediction system 102 via aschematic-flow diagram. For instance, FIG. 2 shows one or moreembodiments of a simplified sequence-flow that the prediction system 102utilizes to train machine learning models, generate one or morepredictive models (e.g., predictive algorithms) for predicting metrologyparameters and/or cell metrics (e.g., cell health), and predictmetrology parameters and/or cell metrics for at least one wafer. As usedherein, the phrase a “predictive model” may refer to a trained machinelearning model for predicting (e.g., estimating) metrology parametersand cell metrics for at least one wafer. As will be appreciated by oneof ordinary skill in the art, the values indicated in the predictivemodels may be determined within confidence intervals. Moreover, asdescribed herein, any values determined and/or predicted by theprediction system 102 may be presented within confidence intervals.

In some embodiments, the process 200 may include receiving a first setof image data from the imager system 106, as shown in act 202 of FIG. 2. For instance, the process 200 may include the prediction system 102receiving the first set of image data from the imager system 106. Thefirst set of image data may include images (e.g., actual images) ofevery wafer within a given wafer lot (e.g., wafers to be analyzed). Inother words, the first set of image data may include images of 100% ofthe wafers of the given wafer lot (e.g., a first wafer lot). In one ormore embodiments, the first set of image data may include image data ofa current state (e.g., layer) of the wafers of the given wafer lot(e.g., images collected after the most recent processing step).Additionally, the first set of image data may include a current red,green, and blue (“RGB”) levels (e.g., intensities) and/or ratios of thewafers of the given wafer lot captured by the imager system 106. Inother words, the first set of image data may include both signal andnoise data. As will be understood by one of ordinary skill in the art,the first set of image data may include captured RGB levels (hereinafter“color data”) across each entire wafer of the entire wafer lot.

As noted above, the first set of image data may be collected by emittingLED light at wafers of the wafer lot and collecting reflected light fromthe wafer lot with the imager system 106 (e.g., a camera of the imagersystem 106). Furthermore, as is known in the art, the color data may berepresentative (e.g., may be a function) of wafer features (e.g.,thickness and critical dimensions). Moreover, as will be appreciated byone of ordinary skill in the art, even when wafers within the givenwafer lot are processed via a same manner, the color data of each waferof the given wafer lot may vary significantly and may indicatedifferences in wafer features.

Additionally, the process 200 may include receiving a second set ofimage data from the imager system 106, as shown in act 204 of FIG. 2 .For instance, the process 200 may include the prediction system 102receiving the second set of image data from the imager system 106.Similar to the first set of image data, the second set of image data mayinclude images (e.g., actual images) of every wafer within the givenwafer lot for which the first set of image data was received. In otherwords, the second set of image data may include images of 100% of thewafers of the given wafer lot. In one or more embodiments, the secondset of image data may include images of each of the wafer of the givenwafer lot at previous steps of processing (e.g., level steps). Forinstance, the second set of image data may include images of each of thewafer of the given wafer during earlier processes (e.g., previousapplied masks, laminations, etchings, exposures, patterning, packaging,etc.).

Additionally, the second set of image data may include color data fromthe previous steps of processing. Moreover, the second set of image datamay include a comparison of color data of each wafer of the given waferlot at each level of processing. Accordingly, as will be described ingreater detail below, utilizing comparisons of color data throughoutprocessing steps, the prediction system 102 may determine influences ofearlier features (e.g., thicknesses and critical dimensions) of waferpresent in earlier processing steps of the wafers on current color data.For instance, multiple layers of processing may impact the current colordata of a wafer of the given wafer lot. As a result, the second set ofimage data may include noise data.

In some embodiments, a granularity (e.g., size in which data fields aresub-divided) in the image data is smaller than a wafer level (i.e., dielevel or sub-die level or point level). As is discussed in greaterdetail below, a metrology measurement for a particular point in a givenwafer may be matched to measured RGB values and image data of the die ator next to the particular point of the given wafer. Both the metrologydata and the RGB data of the image data within a given wafer can varyfrom point to point. In some embodiments, point level data (i.e., datafrom each measurement point of a wafer) can be considered separately foranalysis.

Furthermore, the process 200 may include receiving measured metrologydata from the metrology equipment 104, as shown in act 206 of FIG. 2 .For example, the process 200 may include the prediction system 102receiving measured metrology data from the metrology equipment 104. Asdiscussed above, in some embodiments, the metrology equipment 104 mayinclude one or more of a scatterometer or ellipsometer. As noted above,the metrology data may include measured metrology parameters such as,thicknesses of features (e.g., layers and/or films) of the given waferand/or dimensions of features (e.g., patterns, scratches, topography,masks, lines, holes, marks) of a wafer measured via conventionalmethods.

In some embodiments, the measured metrology data may include measuredmetrology data of the same wafer lot for which the first and secondimage data sets were received. As mentioned above, due to timeconstraints, utilizing traditional metrology equipment (e.g., ascatterometer and/or ellipsometer) limits a number of data points thatcan reasonably be collected within a given time frame. Accordingly, evenwhen the measured metrology data correlates to the same wafer lot forwhich the first and second image data sets were received, the measuredmetrology data may not include data for every wafer of the given waferlot. For instance, in some embodiments, the received measured metrologydata may have only been gathered intermittently, from only portions ofwafers, from only a portion of the wafers of the given wafer lot, etc.

In additional embodiments, the measured metrology data may not correlateto the same wafer lot for which the first and second image data setswere received. For instance, the measured metrology data may correlateto previously received image data for other analyzed wafer lots. As isdescribed in greater detail below, the measured metrology data set maybe utilized by the prediction system 102 to train a machine learningmodel and to generate one or more predictive models. Additionally, theprediction system 102 may utilize the metrology data to validatepreviously generated predictive models. As non-limiting examples, theprediction system 102 may receive image data (e.g., first and secondsets of image data) and metrology data for a first wafer lot. Asdiscussed in greater detail below, the prediction system 102 may utilizethe image data and the metrology data of the first wafer lot to trainmachine learning models (i.e., the predictive models). Furthermore, theprediction system 102 may receive only image data for a second waferlot, third wafer lot, fourth wafer lot, etc. However, in someembodiments, the prediction system 102 may intermittently receivemetrology data for later wafer lots.

Referring still to FIG. 2 , the process 200 may further include, uponreceiving the first and second sets of image data, filtering the firstand second sets of image data, as shown in act 205 of FIG. 2 . Forinstance, the prediction system 102 may filter the first and second setsof image data into at least three categories. For example, theprediction system 102 may filter the first and second sets of image datainto training image data (as shown in act 207), validation image data(as shown in act 209), and image data that has no available and/ormeasured correlating metrology data (referred to hereinafter a“metro-less image data”) (as shown in act 211). Additionally, withineach of the above-mentioned categories, the prediction system 102 maygroup the data based on how the individual wafers were previouslyprocessed (e.g., etched, exposed, layered, formed, etc.). Moreover, theprediction system 102 may identify and remove outliers (e.g., outlierdie data) from each category. For instance, if one or more wafers withinthe given wafer lot were processed differently than any other waferwithin the given wafer lot, the prediction system 102 may identify theone or more wafers as outlier data. Additionally, the prediction system102 may apply one or more fitness functions (e.g., averages analyses,mean square analyses, average correlation coefficient analyses,performance index analyses, least squared error analyses, etc.) to theimage data (i.e., the first and second sets of image data) to identifyoutliers. Furthermore, the prediction system 102 may exclude anyidentified outliers from further analysis and modeling.

Additionally, the process 200 may include filtering the receivedmeasured metrology data, as shown in act 208 of FIG. 2 . For example,the prediction system 102 may filter the measured metrology data into atleast two categories. In particular, the prediction system 102 mayfilter the measured metrology data into training metrology data (asshown in act 210) and into validation metrology data (as shown in act212). As is discussed in greater detail below, the prediction system 102may utilize the training metrology data to train a machine learningmodel and generate one or more predictive models for predictingmetrology parameters and cell metrics data for a given wafer lot.Furthermore, the prediction system 102 may utilize the validationmetrology data to validate predictions made by the prediction system 102in regard to metrology parameters and cell metrics. In one or moreembodiments, filtering the measured metrology data may includeidentifying and removing outliers from the measured metrology data. Forexample, the prediction system 102 may apply any of the above describedfitness functions or any other known method of identifying outliers tothe measured metrology data.

In one or more embodiments, the process 200 may include applying one ormore machine learning techniques to the received training image data andthe metrology training data of the given wafer lot, as shown in act 213of FIG. 2 . For example, the prediction system 102 may apply one or moremachine learning techniques to the received training image data and themetrology training data of the given wafer lot (e.g., at least one waferlot). In one or more embodiments, the machine learning techniques mayinclude one or more of regression models (e.g., a set of statisticalprocesses for estimating the relationships among variables),classification models, and/or phenomena models. Additionally, themachine-learning techniques may include a quadratic regression analysis,a logistic regression analysis, a support vector machine, a Gaussianprocess regression, ensemble models, or any other regression analysis.Furthermore, in yet further embodiments, the machine-learning techniquesmay include decision tree learning, regression trees, boosted trees,gradient boosted tree, multilayer perceptron, one-vs-rest, Naïve Bayes,k-nearest neighbor, association rule learning, a neural network, deeplearning, pattern recognition, or any other type of machine learning. Inyet further embodiments, the machine-learning techniques may include amultivariate interpolation analysis.

Furthermore, by applying the one or more machine-learning techniques tothe received training image data and the training metrology data of thegiven wafer, the process 200 may further include generating one or morepredictive models for predicting metrology parameters of wafer lots(e.g., the given wafer lot and other wafer lots) based on received imagedata, as shown in act 214 of FIG. 2 . For instance, the predictionsystem 102 may utilize the image training data and the metrologytraining data to train one or more predictive models (e.g., predictivealgorithms) to predict metrology data from image data of wafers. Inother words, via the machine learning model techniques, the predictionsystem 102 may learn correlations between the image data (e.g., colordata) and the metrology data (e.g., features data) of wafers. Putanother way, the prediction system may learn the relationship betweenthe image data and the metrology data of wafers. For example, as will beunderstood in the art, for a given set of input values (e.g., the imagedata) of a given wafer lots (e.g., images of 100% of the wafer lots),the prediction system 102 and generated predictive models are expectedto produce the same output values (i.e., metrology data (thicknesses,critical dimensions, cell-metrics)) as is actually measured via thetraditional metrology equipment described above. In particular, thepredictive models are trained to produce the values for a given set ofinput values (e.g., the image data) of at least one wafer lot thatcorresponds to the values measured by the metrology equipment byiterating the training process for a relatively large number of inputvalue sets. In other words, the predictive models are trained againstthe training metrology data. After a sufficient number of iterations,the predictive models become trained predictive models. As is discussedin greater detail below, once the predictive models have been generatedand trained, received metrology data may only be used for validating thepredictive models, and in some instances, re-training the predictivemodels. After being trained, the trained predictive models may then beutilized by the prediction system 102 to simulate or predict (e.g.,estimate) metrology data (referred to below as “virtual metrology data”)from image data of a wafer lot. The predictive models may be trained viaany manner known in the art. Furthermore, although the predictive modelsare described herein as being trained on data from a wafer lot, thedisclosure is not so limited. Rather, the predictive models may also betrained on historical data (e.g., data (image and metrology data) fromprevious analyses performed on other wafers and/or wafer lots).

Referring still to FIG. 2 , the process 200 may include applying the oneor more predictive models to metro-less image data (e.g., image datafrom wafer lots not having correlating metrology data), as shown in act216 of FIG. 2 . For example, the process 200 may include the predictionsystem 102 applying the one or more predictive models to metro-lessimage data. In some embodiments, applying the one or more predictivemodels to the metro-less image data may include applying the predictivemodels to an entirety of the metro-less image data (e.g., 100%) of thewafer lot.

By applying the one or more predictive models to the metro-less imagedata, the process 200 may include determining and generating virtualmetrology data (e.g., predicted and/or estimated metrology data) for thewafer lot for which the metro-less image data was received, as shown inact 218 of FIG. 2 . For example, based on the received metro-less imagedata for the given wafer lot, the prediction system 102 may, via thegenerated predictive models, predict correlating virtual metrology dataand/or virtual cell metrics data of the given wafer lot without takingany measurements with the metrology equipment 104. In some embodiments,the virtual metrology data may include data related to predictedthicknesses and/or critical dimensions of features of the wafer lot. Forexample, the virtual metrology data may include data related to any ofthe measurements achievable via traditional metrology equipment, asdescribed above. Furthermore, the virtual metrology data may includedata related to any of the cell metrics described above, such as, forexample, median threshold voltage of a given die, variation in thresholdvoltage within the given die, operating voltage window of the given die,endurance to read and/or write cycles, lifetime of the given die,persistence of memory within the die, binning of the given die tovarious product grades. For instance, the virtual metrology may includeany cell metrics data determinable via traditional metrology equipment.Additionally, the virtual metrology data may include predictionintervals (e.g., an estimate of an interval in which a prediction (e.g.,a future observation) will fall, with a certain probability, given whathas already been observed) related to any of the described virtualmetrology data. In some embodiments, the generated virtual metrology mayinclude one or more statistical process control (“SPC”) charts. The SPCcharts may include output data (e.g., virtual metrology data) plottedagainst wafer lots. The virtual metrology data and virtual cell metricdata are described in greater detail below in regard to FIGS. 3A-5B.

In view of the foregoing, and as discussed briefly above in regard toact 214 of FIG. 2 , the prediction system 102, via the generatedpredictive models, may simulate or predict (e.g., estimate) metrologydata for a given set of input values (e.g., image data) of the givenwafer set without having any metrology data from traditional metrologyequipment. Therefore, unlike conventional imager systems, which arelimited to capturing image data, the prediction system 102 of thepresent disclosure can extract metrology data and cell metrics data fromimage data with a precision similar to precisions achieved usingtraditional metrology equipment. Furthermore, because the predictionsystem 102 is primarily utilizing image data after training thepredictive models, the prediction system 102 provides significantlyfaster times in determining metrology data of wafer lots in comparisonto traditional metrology equipment. Therefore, the prediction system 102may lead to cost savings and faster overall processing of semiconductordevices. Furthermore, the prediction system 102 of the presentdisclosure may lead to better process control (e.g., utilizing feedbackand feedforward mechanisms). Additionally, the prediction system 102 ofthe present disclosure may lead to identifying process and tool driftsand mismatches. Likewise, the prediction system 102 of the presentdisclosure may lead to better predictions of die loss due to theincrease in available metrology data of the wafer lot.

Furthermore, because measurements via traditional metrology equipmentrequire a significant amount of time to acquire, the prediction system102 of the present disclosure, given a similar amount of time, maygenerate and predict more than one hundred times an amount of metrologydata for a given wafer lot in comparison to the traditional metrologyequipment. Moreover, because the predictive models of the predictionsystem 102 are trained against measured metrology data, the predictionsystem 102 may lead to increasing accuracy (e.g., understanding) ofmetrology data provided by the metrology equipment 104. By reducing useand/or the necessity of traditional metrology equipment, the predictionsystem 102 of the present disclosure provides an efficient method todetermine metrology data and cell metrics data of every die of everywafer of a wafer lot while reducing costs and processing times.

In view of the foregoing, the prediction system of the presentdisclosure may generate virtual metrology data based on image data forwafer lots for which minimal or no metrology data is available.Accordingly, in comparison to traditional methods of determiningmetrology data of wafer lots, the prediction system 102 of the presentdisclosure requires significantly less measured metrology data.

Still referring to FIG. 2 , the process 200 may further include applyingthe one or more predictive models to the image validation data of awafer lot, as shown in act 220 of FIG. 2 . For instance, the predictionsystem 102 may apply the one or more predictive models to the imagevalidation data via any of the manners described above in regard to act216 of FIG. 2 . By applying the one or more predictive models to theimage validation data, the process 200 may include generating virtualvalidation metrology data for the image validation data, as shown in act222 of FIG. 2 . For example, based on the available image validationdata for a given wafer lot, the prediction system 102 may apply thepredictive models to determine and/or predict virtual validationmetrology data and/or cell metrics data of the given wafer lot withouttaking any measurements with the metrology equipment. The virtualvalidation metrology data may include any of the data described above inregard to the virtual metrology data and virtual cell metrics data andact 218 of FIG. 2 .

In some embodiments, the process 200 may also include validating thevirtual validation metrology data against the measured validationmetrology data, as shown in act 224 of FIG. 2 . For instance, thegenerated virtual validation metrology may include one or more goodnessof fit (“GOF”) charts. In other words, the prediction system 102 maydetermine a goodness of fit of the output data (e.g., virtual metrologydata) and measured data (e.g., measured metrology data). As is known inthat art, goodness of fit is a component of regression analysis, whichis a statistical method used to make predictions based on observedvalues. In other words, goodness of fit is a measurement of howcorrelated a group of actual observations (i.e., measured metrologydata) are to a model's predictions (i.e., the virtual metrology data).For instance, the prediction system 102 may compare the virtualvalidation metrology data to the measured validation metrology data viaone or more GOF charts.

As is known in the art, a level of a goodness of fit is represented bythe coefficient of determination (R²), which ranges between 0.0 and 1.0with higher percentages indicates better fits. In some embodiments, ifthe prediction system 102 determines that virtual validation metrologydata for a wafer lot indicates a low goodness of fit with the measuredvalidation metrology data, the prediction system 102 may triggeradditional metrology data to be collected via the metrology equipmentfor further validation and correction (e.g., re-training of the machinelearning model). For instance, if the goodness of fit indicates acoefficient of determination (R²) below a particular threshold (e.g.,0.80, 0.60, or 0.50), the prediction system 102 may trigger additionalmetrology data to be collected via the metrology equipment for furthervalidation and correction of the predictive models. Additionally, in oneor more embodiments, if the prediction system 102 determines that lowgoodness of fits are measured consecutively for a threshold number ofmultiple wafers or wafer lots, the prediction system 102 may trigger are-training (e.g., a redo of the predictive model validation) of thepredictive models. In additional embodiments, the prediction system 102may validate the virtual validation metrology data against the measuredvalidation metrology data via a root-mean-square error (“RMSE”)analysis. In such embodiments, if the RMSE is above a particularthreshold number, the prediction system 102 may trigger a re-training ofthe predictive models. As a result of the foregoing, the predictionsystem 102 may be continuously verifying the predictive models andre-training the predictive models to maximize accuracy of itspredictions. If a model drift cannot be suitably identified using thecoefficient of determination (R²) or RMSE, the prediction system 102 maytrigger a periodic re-training of the predictive models. The predictivemodels may be re-trained via any methods known in the art. In someembodiments, the prediction system 102 may automatically re-train thepredictive models at given intervals of time (e.g., every hour, day,week, month, etc.). In additional embodiments, the prediction system 102may re-train the predictive models on an ad hoc basis if underlyingmaterial properties of a wafer lot change for, e.g., new cellintroductions.

Although specific methods of validating the virtual validation metrologydata against the measured validation metrology data are describedherein, the disclosure is not so limited. Rather, any methods and/oranalyses known in the art for comparing predictive data to measured datamay be utilized by the prediction system 102.

Referring still to FIG. 2 , the process 200 may further include errorcorrections of the virtual metrology data, as shown in act 226 of FIG. 2. For instance, the process 200 may include the prediction system 102applying any of the above-described fitness functions to the data of thevirtual metrology data to determine outliers, random error, systematicerror, or any other type of error. For instance, the prediction system102 may apply an averages analysis, a mean square analysis, averagecorrelation coefficient analysis, performance index analysis, leastsquared error analysis, a standard deviation analysis, z-score orextreme value analysis, proximity based non-parametric models, highdimensional outlier detection methods, etc. In addition to determiningoutliers, the prediction system 102 may correct and/or remove theoutliers from further analysis and modeling.

FIGS. 3A and 3B show example comparisons of metrology data acquired viaconventional methods (FIG. 3A) and virtual metrology data generated bythe prediction system 102 of the present disclosure (FIG. 3B) viapredictive models applied to image data. As shown in FIG. 3A, and willbe appreciated by one of ordinary skill in the art due to the timeconstraints of traditional metrology equipment, the data points (e.g.,for the measured wafers of a wafer lot) are limited and data points arenot included for each wafer. Conversely, as shown in FIG. 3B, thevirtual metrology data generated from the trained predictive models andbased on image data of the wafers of the wafer lot includessignificantly more data points for each (i.e., every) wafer of the waferlot. Furthermore, as will be appreciated by one of ordinary skill in theart, more data leads to better processing, better predictions of dieperformance, better dead die detection, etc. For instance, as anon-limiting example, traditional metrology equipment may provide forabout 100 data points for a given wafer lot, and the prediction system102 of the present disclosure may provide about 4200 data points for thesame given wafer lot. Additionally, traditional metrology equipment maynot provide data points for each wafer within the given wafer lot, andthe prediction system 102 of the present disclosure may provide datapoints for every wafer of the wafer lot.

FIGS. 4A and 4B show additional comparisons of metrology data acquiredvia conventional methods (FIG. 4A) and virtual metrology data generatedby the prediction system 102 of the present disclosure (FIG. 4B). Forinstance, FIG. 4A shows an example contour plot of metrology dataacquired for a given wafer of a wafer lot via traditional metrologyequipment, and FIG. 4B shows an example contour plot of virtualmetrology data determined and generated via the predictive modelsdescribed above. As shown in FIGS. 4A and 4B, the virtual metrology dataprovides a significantly more defined contour plot in comparison themeasured metrology data.

FIGS. 5A and 5B show additional comparisons of metrology data acquiredvia conventional methods (FIG. 5A) and virtual metrology data generatedby the prediction system 102 of the present disclosure (FIG. 5B). Forinstance, FIG. 4A shows an example variability plot of metrology dataacquired for a given wafer of a wafer lot via traditional metrologyequipment, and FIG. 4B shows an example variability plot of virtualmetrology data determined and generated via the predictive modelsdescribed above. As shown in FIGS. 5A and 5B, the virtual metrology dataprovides a significantly more defined variability plot in comparison themeasured metrology data. Furthermore, as shown in FIGS. 5A and 5B, theprediction system 102 of the present disclosure may be utilized to fillin gaps within measured metrology data. For example, in someembodiments, minimal metrology data may be available for a given waferlot, and the prediction system 102 of the present disclosure may beutilized to supplement and augment the metrology data with virtualmetrology data via any of the manners described above.

FIG. 6 shows a schematic representation of an imager system 600according to one or more embodiments of the present disclosure. As shownin FIG. 6 , the imager system 600 may include a tunable light source602, a diffuser 604, a mirror 606, one or more photo diodes 608, acontroller 610, a light source power supply 612, and a charge-coupleddevice (CCD) camera 614.

As will be understood by one of ordinary skill in the art, traditionallight sources of imager systems include white LED light sources.Furthermore, the emission spectrums of the conventional white LED lightssources are typically not flat and can skew intensities of individualcolors (e.g., red, green, and blue) within white light emitted by thewhite LED light sources. Skewing intensities of the individual colorscan skew image data and/or resulting virtual metrology data (describedabove). Accordingly, the imager system 600 described in regard to FIG. 6provides for balancing intensities of the colors (e.g., red, green, andblue) for all wavelengths within the white light such that imager system600 can be cross-calibrated with traditional metrology equipment andprovide consistent and reliable image data.

The tunable light source 602 may be oriented to emit light on a wafer616 disposed on a chuck 618 at an acute and/or obtuse angle. Thediffuser 604 may be disposed between the tunable light source 602 andthe wafer 616 and may be configured to diffuse light emitted by thetunable light source 602. In one or more embodiments, the photo diodes608 may be disposed within the diffuser 604 or proximate to the diffuser604 to detect and capture light emitted by the tunable light source 602.The photo diodes 608 may be operably coupled to and may be incommunication with the controller 610. Additionally, the light sourcepower supply 612 may be operably coupled to the controller 610 and maybe operated by the controller 610.

The mirror 606 may be oriented above the wafer 616 and/or chuck 618 andmay be oriented to reflect light reflected off the wafer 616 toward toCCD camera 614. The CCD camera 614 may capture image data (e.g., any ofthe image data described above in regard to FIGS. 1-5B) of the wafer 616and may provide the image data to the controller 610.

In some embodiments, the tunable light source 602 may include an arrayof LEDs. Furthermore, each of the LEDS may be tunable. In other words,an intensity of the LED may be tunable (e.g., adjustable). Morespecifically, the intensities of individual wavelengths of the lightsource may be tunable across an entire illuminated area of the imagersystem 600. For example, if a first portion of the illuminated area hasdifferent RGB intensities than a second portion of the illuminated area,the intensities of individual wavelengths of the light source may betunable to achieve desired RGB intensities for each portion of theilluminated area. In one or more embodiments, the CCD camera 614 mayexhibit a stable spectral response. Furthermore, the CCD camera 614 mayinclude a hermetically sealed two-stage cooled color camera.Additionally, in some embodiments, the mirror 606 may include one ormore of protected gold, protected silver, UV-enhanced aluminum, orprotected aluminum. For instance, the mirror may include protectedsilver.

In some embodiments, one or more of the CCD camera 614 and the photodiodes 608 may provide feedback to the controller 610 in regard tointensities of colors of light emitted by the tunable light source 602.For instance, one or more of the CCD camera 614 and the photo diodes 608may provide data to the controller 610 indicating a current intensity ofeach color (e.g., red, blue, and green) of the light emitted by thetunable light source 602.

Furthermore, based on the feedback received from the CCD camera 614and/or photo diodes 608, the controller 610 may adjust current (e.g.,electrical current) to one or more LEDs of the tunable light source 602via the power supply 612. For instance, the controller 610 may adjustcurrent to one or more LEDs of the tunable light source 602 such thatthe intensities of the colors of light emitted by the tunable lightsource 602 are substantially the same. For example, the controller 610may adjust the current to one or more LEDs of the tunable light source602 to achieve the intensities depicted in FIG. 7 . In some embodiments,the imager system 600 may provide relatively high resolution images ofthe wafer 616. For instance, the imager system 600 may provide imageshaving high enough resolutions to differentiate from an array region andperiphery region of a given die (e.g., die level resolution). As anon-limiting example, the imager system 600 may provide images havingbetween eight and twelve megapixel resolutions. Additionally, thecontroller 610 of the imager system 600 may utilize computer drawings(e.g., computer-aided drawings) of die design to identify die bordersand array borders from the computer drawings. Furthermore, in someembodiments, the controller 610 may generate a mean, median and standarddeviation of individual intensities of colors of light reflected to theCCD camera 614 from each die, array of dies excluding periphery, andsub-array regions.

In some embodiments, the imager system 600 of the present disclosure mayenable determinations die level mean, median, and standard deviation ofthe RGB values of the pixels of the images of the wafer. For instance,the imager system 600 of the present disclosure may enabledeterminations die level mean, median, and standard deviation of the RGBvalues of the pixels from only the array region of given die. Pixels ofthe images from the periphery region of the die, scribe, and allnon-array region may be removed before generating any image data.Subsequent process may include sub-die level image data where the mean,median, and standard deviation of the pixels of the images aredetermined for individual array banks within a given die.

FIG. 8 is a schematic representation of a wafer 616 having the mirror606 disposed thereover and an illumination area 620 of the tunable lightsource 602 on the wafer 616. In some embodiments, the CCD camera 614 maycapture rectangular images uniformly lit (e.g., same intensity for eachcolor) across the imaged area by the tunable light source 602. Movementof the wafer 616 enables multiple images to be collected. The multipleimages can be aligned together to create an entire wafer image. Themirror 606, tunable light source 602, and handler (e.g., wafer handler)configuration may include any configuration known in the art.

FIG. 9 is a block diagram of a prediction system 102 and/or a userdevice 110 according to one or more embodiments of the presentdisclosure. One will appreciate that one or more computing devices 900may implement the prediction system 102 and/or a user device 110. Theprediction system 102 and/or a user device 110 can comprise a processor902, a memory 904, a storage device 906, an I/O interface 908, and acommunication interface 910, which may be communicatively coupled by wayof a communication infrastructure 912. While an example of a computingdevice is shown in FIG. 9 , the components illustrated in FIG. 9 are notintended to be limiting. Additional or alternative components may beused in other embodiments. Furthermore, in certain embodiments, thecomputing device 900 can include fewer components than those shown inFIG. 9 . Components of the computing device 900 shown in FIG. 9 will nowbe described in additional detail.

In one or more embodiments, the processor 902 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions, theprocessor 902 may retrieve (or fetch) the instructions from an internalregister, an internal cache, the memory 904, or the storage device 906and decode and execute them. In one or more embodiments, the processor902 may include one or more internal caches for data, instructions, oraddresses. As an example and not by way of limitation, the processor 902may include one or more instruction caches, one or more data caches, andone or more translation lookaside buffers (TLBs). Instructions in theinstruction caches may be copies of instructions in the memory 904 orthe storage device 906.

The memory 904 may be used for storing data, metadata, and programs forexecution by the processor(s). The memory 904 may include one or more ofvolatile and non-volatile memories, such as Random Access Memory(“RAM”), Read-Only Memory (“ROM”), a solid state disk (“SSD”), Flash,Phase Change Memory (“PCM”), or other types of data storage. The memory904 may be internal or distributed memory.

The storage device 906 includes storage for storing data orinstructions. As an example and not by way of limitation, storage device906 can comprise a non-transitory storage medium described above. Thestorage device 906 may include a hard disk drive (HDD), a floppy diskdrive, flash memory, an optical disc, a magneto-optical disc, magnetictape, or a Universal Serial Bus (USB) drive or a combination of two ormore of these. The storage device 906 may include removable ornon-removable (or fixed) media, where appropriate. The storage device906 may be internal or external to the computing device 900. In one ormore embodiments, the storage device 906 is non-volatile, solid-statememory. In other embodiments, the storage device 906 includes read-onlymemory (ROM). Where appropriate, this ROM may be mask programmed ROM,programmable ROM (PROM), erasable PROM (EPROM), electrically erasablePROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or acombination of two or more of these.

The I/O interface 908 allows a user 111 to provide input to, receiveoutput from, and otherwise transfer data to and receive data fromcomputing device 900. The I/O interface 908 may include a mouse, akeypad or a keyboard, a touch screen, a camera, an optical scanner,network interface, modem, other known I/O devices or a combination ofsuch I/O interfaces. The I/O interface 908 may include one or moredevices for presenting output to a user 111, including, but not limitedto, a graphics engine, a display (e.g., a display screen), one or moreoutput drivers (e.g., display drivers), one or more audio speakers, andone or more audio drivers. In certain embodiments, the I/O interface 908is configured to provide graphical data to a display for presentation toa user 111. The graphical data may be representative of one or moregraphical user interfaces and/or any other graphical content as mayserve a particular implementation.

The communication interface 910 can include hardware, software, or both.In any event, the communication interface 910 can provide one or moreinterfaces for communication (such as, for example, packet-basedcommunication) between the computing device 900 and one or more othercomputing devices or networks. As an example and not by way oflimitation, the communication interface 910 may include a networkinterface controller (NIC) or network adapter for communicating with anEthernet or other wire-based network or a wireless MC (WNIC) or wirelessadapter for communicating with a wireless network, such as a WI-FI.

Additionally or alternatively, the communication interface 910 mayfacilitate communications with an ad hoc network, a personal areanetwork (PAN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), or one or more portions of the Internetor a combination of two or more of these. One or more portions of one ormore of these networks may be wired or wireless. As an example, thecommunication interface 910 may facilitate communications with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination thereof.

Additionally, the communication interface 910 may facilitatecommunications various communication protocols. Examples ofcommunication protocols that may be used include, but are not limitedto, data transmission media, communications devices, TransmissionControl Protocol (“TCP”), Internet Protocol (“IP”), File TransferProtocol (“FTP”), Telnet, Hypertext Transfer Protocol (“HTTP”),Hypertext Transfer Protocol Secure (“HTTPS”), Session InitiationProtocol (“SIP”), Simple Object Access Protocol (“SOAP”), ExtensibleMark-up Language (“XML”) and variations thereof, Simple Mail TransferProtocol (“SMTP”), Real-Time Transport Protocol (“RTP”), user device 110Datagram Protocol (“UDP”), Global System for Mobile Communications(“GSM”) technologies, Code Division Multiple Access (“CDMA”)technologies, Time Division Multiple Access (“TDMA”) technologies, ShortMessage Service (“SMS”), Multimedia Message Service (“MMS”), radiofrequency (“RF”) signaling technologies, Long Term Evolution (“LTE”)technologies, wireless communication technologies, in-band andout-of-band signaling technologies, and other suitable communicationsnetworks and technologies.

The communication infrastructure 912 may include hardware, software, orboth that couples components of the computing device 900 to each other.As an example and not by way of limitation, the communicationinfrastructure 912 may include an Accelerated Graphics Port (AGP) orother graphics bus, an Enhanced Industry Standard Architecture (EISA)bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, anIndustry Standard Architecture (ISA) bus, an INFINIBAND interconnect, alow-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture(MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express(PCIe) bus, a serial advanced technology attachment (SATA) bus, a VideoElectronics Standards Association local (VLB) bus, or another suitablebus or a combination thereof.

One or more embodiments of the present disclosure include a method ofpredicting virtual metrology data for a wafer lot. The method mayinclude receiving first image data from an imager system, the firstimage data relating to at least one first wafer lot; receiving measuredmetrology data from metrology equipment relating to the at least onefirst wafer lot; applying one or more machine learning techniques to thefirst image data and the measured metrology data to generate at leastone predictive model for predicting at least one of virtual metrologydata or virtual cell metrics data of wafer lots; and utilizing the atleast one generated predictive model to generate at least one of firstvirtual metrology data or first virtual cell metrics data for the firstwafer lot.

Some embodiments of the present disclosure include a method ofpredicting virtual metrology data for a wafer lot. The method mayinclude receiving first image data from an imager system, the firstimage data relating to at least one first wafer lot; receiving measuredmetrology data from metrology equipment of the at least one first waferlot; training a machine learning model with the image data and themeasured metrology data; and applying one or more machine learningtechniques to the first image data and the measured metrology data togenerate at least one a predictive model for predicting at least one ofvirtual metrology data or virtual cell metrics data of wafer lots;receiving second image data from the imager system, the second imagedata relating to at least one second wafer lot; and generating, via theat least one generated predictive model, at least one of second virtualmetrology data or second virtual cell metrics data for the second waferlot based on the second image data.

One or more embodiments of the present disclosure include a method ofpredicting virtual metrology data for a wafer lot. The method mayinclude receiving first image data from an imager system, the firstimage data relating to at least one first wafer lot without receivingany measured metrology data related to the at least one first wafer lotutilizing at least one generated predictive model for predicting atleast one of virtual metrology data or virtual cell metrics data ofwafer lots to generate at least one of first virtual metrology data orfirst virtual cell metrics data for the first wafer lot based on thefirst image data.

Additional embodiments of the present disclosure include an imagersystem. The imager system may include a tunable light source, adiffuser, at least one diode, and a controller. The tunable light sourcemay be oriented to emit light at a wafer and may be operably couple to apower supply. The diffuser may be disposed between the tunable lightsource and the wafer. The at least one diode may disposed between thetunable light source and the wafer, and the at least one diode may beconfigured to detect light emitted by the tunable light source. Thecontroller may be configured to receive signals from the at least onediode and, based on the received signals, adjust current being suppliedto the tunable light source via the power supply to balance colorintensities of the light.

The embodiments of the disclosure described above and illustrated in theaccompanying drawings do not limit the scope of the disclosure, which isencompassed by the scope of the appended claims and their legalequivalents. Any equivalent embodiments are within the scope of thisdisclosure. Indeed, various modifications of the disclosure, in additionto those shown and described herein, such as alternate usefulcombinations of the elements described, will become apparent to thoseskilled in the art from the description. Such modifications andembodiments also fall within the scope of the appended claims andequivalents.

What is claimed is:
 1. An imager system, comprising: a tunable lightsource oriented to emit light at a wafer and operably coupled to a powersupply; a diffuser disposed between the tunable light source and thewafer; at least one diode disposed between the tunable light source andthe wafer, the at least one diode configured to detect light emitted bythe tunable light source; and a controller configured to receive signalsfrom the at least one diode and, responsive to the received signals,adjust current being supplied to the tunable light source via the powersupply to balance color intensities of the light.
 2. The imager systemof claim 1, further comprising: a charge-coupled device camera; and amirror oriented to direct reflect light from a surface of the wafertoward to the charge-coupled device camera.
 3. The imager system ofclaim 2, wherein the charge-coupled device camera is configured toprovide image data to the controller, the image data related to thereflected light directed to the charge-coupled device camera by themirror.
 4. The imager system of claim 2, wherein the charge-coupleddevice camera comprises a hermetically sealed two-stage cooled colorcamera.
 5. The imager system of claim 2, wherein the mirror comprisesone or more of protected gold, protected silver, UV enhanced aluminum,or protected aluminum.
 6. The imager system of claim 1, wherein the atleast one diode is disposed within the diffuser.
 7. The imager system ofclaim 1, wherein the at least one diode is disposed adjacent to thediffuser.
 8. The imager system of claim 1, wherein the tunable lightsource is oriented to emit light at the wafer at an acute angle relativeto an upper surface of the wafer.
 9. The imager system of claim 1,wherein the tunable light source comprises an array of LEDs.
 10. Theimager system of claim 9, wherein each LED of the array of LEDs isindividually tunable.
 11. The imager system of claim 9, wherein anintensity of each LED of the array of LEDs is individually tunable. 12.A method comprising: emitting light at a wafer via a tunable lightsource; detecting light emitted by the tunable light source; detectinglight reflected from the wafer; and based at least partially on thedetected light emitted by the tunable light source and the detectedlight reflected from the wafer, adjusting light emitted by the tunablelight source.
 13. The method of claim 12, wherein adjusting lightemitted by the tunable light source comprising adjusting an intensity ofone or more LEDs of the tunable light source.
 14. The method of claim12, wherein emitting light at a wafer via a tunable light sourcecomprises emitting light at the wafer through a diffuser.
 15. The methodof claim 12, wherein detecting light reflected from the wafer comprisesreflecting light reflected from the wafer with a mirror and at acharge-coupled device camera.
 16. The method of claim 12, whereindetecting light emitted by the tunable light source comprises detectingthe light emitted by the tunable light source via a light diode disposedbetween the tunable light source and the wafer.
 17. The method of claim12, further comprising, based at least partially on the detected lightemitted by the tunable light source and the detected light reflectedfrom the wafer, determining an intensity of each color of the lightemitted by the tunable light source.
 18. An imager system comprising: atunable light source; at least one diode disposed between the tunablelight source and a wafer, the at least one diode configured to detectlight emitted by the tunable light source; and a controller configuredto receive signals from the at least one diode and, responsive to thereceived signals, adjust a current being supplied to the tunable lightsource.